# DynamoDB vs. MongoDB: a Complete Comparison in 2025 > An extensive comparison between DynamoDB and MongoDB on performance, usability, operability, ecosystem and more. Adela | 2025-04-17 | Source: https://www.bytebase.com/blog/dynamodb-vs-mongodb/ --- > **Note:** This post is maintained by Bytebase, an open-source database DevSecOps tool that can manage both Amazon DynamoDB and MongoDB. We update the post every year. | Update History | Comment | | -------------- | ---------------- | | 2025/04/17 | Initial version. | NoSQL databases like Amazon DynamoDB and MongoDB overcome the limitations of traditional relational databases in handling scale, flexibility, and performance. While both are document-oriented, they differ in architecture, deployment, and other aspects. ## Amazon DynamoDB Overview DynamoDB, launched by AWS in 2012, evolved from [Amazon's 2007 Dynamo paper](https://dl.acm.org/doi/10.1145/1323293.1294281). DynamoDB is a fully managed, serverless, key-value and document database that delivers single-digit millisecond performance at any scale. As an AWS service, it eliminates the operational burden of managing database infrastructure, allowing developers to focus on application development. ## MongoDB Overview MongoDB, created in 2007 by 10gen (now MongoDB Inc.), emerged from the founders' struggles with relational databases. Released in 2009 as open-source, it became a leading NoSQL database for flexible, scalable applications. MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents. It offers a rich query language, high availability through replica sets, and horizontal scalability through sharding. ## Comparison Table | Feature | Amazon DynamoDB | MongoDB | | ------------------------- | -------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | | **Type** | Fully managed NoSQL database service | Document-oriented NoSQL database | | **Deployment** | AWS cloud only | Self-hosted, MongoDB Atlas (cloud), or other cloud providers | | **Data Model** | Key-value and document | Document-oriented | | **Schema** | Schema-flexible with required primary key | Fully schema-flexible | | **Document Size Limit** | 400KB per item | 16MB per document | | **Query Language** | Limited query expressions with PartiQL support | Rich query language with MongoDB Query API | | **Indexing** | Primary key, Local Secondary Indexes (LSI), Global Secondary Indexes (GSI) | Multiple index types (single field, compound, multikey, geospatial, text, hashed) | | **Transactions** | ACID transactions across multiple items | ACID transactions across multiple documents | | **Consistency** | Configurable: eventual or strong consistency | Configurable consistency levels | | **Scaling** | Automatic, unlimited horizontal scaling | Manual or automated sharding | | **Global Distribution** | Global Tables for multi-region replication | Global Clusters in Atlas | | **Pricing Model** | Pay-per-request or provisioned capacity | Instance-based pricing in Atlas, free Community Edition | | **Backup & Recovery** | Continuous backups with point-in-time recovery | Continuous backups in Atlas, manual in Community Edition | | **Security** | AWS IAM integration, encryption at rest and in transit | Role-based access control, field-level encryption, TLS/SSL | | **Performance** | Single-digit millisecond response times | Performance varies based on configuration | | **Monitoring** | CloudWatch integration | MongoDB Atlas monitoring, self-monitoring tools | | **Use Cases** | High-scale applications with simple access patterns | Applications requiring complex queries and flexible schema | | **Ecosystem Integration** | Deep AWS service integration | Broad language support and third-party integrations | | **Vendor Lock-in** | High (AWS-specific) | Moderate (portable between environments) | ## Key Comparison Points ### Data Model and Schema Flexibility **DynamoDB**: - Supports key-value and document models - Requires defining primary key structure at table creation - Flexible schema for non-key attributes - 400KB maximum item size ```json { "product_id": "P100", "name": "Wireless Headphones", "price": 99.99, "attributes": { "color": "black", "weight": "250g" } // Cannot efficiently store embedded reviews (400KB limit) } ``` **MongoDB**: - Document-oriented - Fully flexible schema - Supports nested documents and arrays - 16MB maximum document size ```json { "_id": "P100", "name": "Wireless Headphones", "price": 99.99, "attributes": { "color": "black", "weight": "250g" }, "reviews": [ // Nested array { "user": "Alice", "rating": 5 }, { "user": "Bob", "rating": 4 } ] // 16MB max allows complex hierarchies } ``` **Analysis**: MongoDB offers greater schema flexibility and document complexity, making it better suited for applications with evolving data structures or complex hierarchical data. DynamoDB's more rigid primary key requirements provide performance benefits but less flexibility. BSON is a binary serialization format that is more efficient for MongoDB. ### Deployment and Management **DynamoDB**: - Fully managed AWS service - Serverless architecture - No infrastructure to manage - AWS-only deployment **MongoDB**: - Self-hosted option (MongoDB Community Server) - Managed service option (MongoDB Atlas) - Available on multiple cloud providers - Requires more operational expertise for self-hosted deployments **Analysis**: DynamoDB eliminates operational overhead but ties you to AWS. MongoDB offers more deployment flexibility but may require more management effort, especially for self-hosted deployments. ### Scalability and Performance **DynamoDB**: - **Auto-scaling:** Handles traffic spikes automatically - **Predictable speed:** Always fast (single-digit ms responses) - **Zero maintenance:** No partitions or shards to manage - **Pay options:** Choose fixed capacity or pay-per-use **MongoDB**: - **Flexible scaling:** Can grow vertically or horizontally - **Performance tuning:** Depends on your setup (indexes, shards) - **More control:** You decide how to split data (shard key strategy) - **Manual effort:** Needs planning for best performance **Analysis**: DynamoDB is better for apps needing instant and maintenance-free scaling, while MongoDB is better for complex systems where you want fine-grained control. ### Query Capabilities **DynamoDB**: - Optimized for **fast key-value lookups** (primary key access) - Basic filtering (limited to indexed attributes) - Supports PartiQL for simple SQL-like queries - Minimal native aggregation support **MongoDB**: - **Expressive query language** with support for complex conditions - **Full aggregation pipeline**(grouping, joins, transformations) - Built-in **text search** and **geospatial queries** **Analysis**: MongoDB’s rich querying features make it ideal for applications needing advanced search, analytics, or hierarchical data. DynamoDB excels in predictable, high-speed key access but lacks flexibility for complex operations. ### Consistency and Transactions **DynamoDB**: - **Consistency Options:** Choose between eventual (faster) or strong (most up-to-date) reads. - **Transactions:** Guarantees ACID compliance for operations across multiple items (e.g., updating inventory and orders together). - **AWS Integration:** Works seamlessly with Lambda, EventBridge, and other AWS services. **MongoDB**: - **Read Control:** Adjustable read isolation levels for different use cases. - **Transactions:** Supports ACID across multiple documents (e.g., transferring money between accounts). - **Real-Time Updates:** Change streams allow event-driven applications (e.g., notifications on data changes). **Analysis**: DynamoDB is best for AWS-based apps needing fast, simple transactions. MongoDB is better for complex document workflows requiring fine-tuned control. ### Global Distribution **DynamoDB**: - Global Tables for multi-region replication - Active-active setup (read/write in any region) - Conflict resolution handled by AWS **MongoDB Atlas**: - Deploy across multiple cloud providers (AWS, Azure, GCP) - Zone sharding for data locality (keep data near users) - Tunable replication for performance vs. cost **Analysis**: DynamoDB's Global Tables offer plug-and-play multi-region scaling on AWS. MongoDB Atlas supports cross-cloud deployments with granular geo-control (GDPR, etc.). ### Pricing Model **DynamoDB - Pay What You Use**: - **Compute:** Pay per request (on-demand) or reserve capacity - **Storage:** $0.25/GB/month - **Bonus:** Scales to $0 when idle (on-demand mode) - **Watch Out:** Data transfer/global replication fees **MongoDB Atlas - Predictable Pricing**: - **Compute:** Fixed-rate vCPU/RAM clusters - **Storage:** 0.08–0.30/GB/month (depends on tier) - **Always On:** Minimum costs apply 24/7 - **Extras:** Backup storage, cross-cloud replication **Analysis**: DynamoDB wins for unpredictable workloads (viral apps, event-driven systems); MongoDB better for steady traffic(SaaS apps, analytics databases). ### Security Features **DynamoDB**: - **Access Control:** AWS IAM for fine-grained permissions - **Network Security:** VPC endpoints for private access - **Encryption:** Always encrypted (at rest/in transit) - **Audit Logs:** Integrated with AWS CloudTrail **MongoDB**: - **Access Control:** Role-based (user/database-level) - **Data Protection:** Field-level encryption - **Network Security:** TLS/SSL for all connections - **Audit Logs:** Built-in database auditing **Analysis**: Both databases offer comprehensive security features, with DynamoDB leveraging AWS's security ecosystem and MongoDB providing more database-specific security controls. ## Use Case Suitability ### When to Choose DynamoDB DynamoDB is particularly well-suited for: 1. **High-Scale Applications**: Applications requiring unlimited scale with consistent performance. 2. **Serverless Architectures**: Applications built on AWS Lambda and other serverless technologies. 3. **Simple Access Patterns**: Applications with straightforward, key-based data access patterns. 4. **AWS-Integrated Applications**: Applications heavily integrated with other AWS services. 5. **IoT and Time-Series Data**: Applications collecting and analyzing large volumes of time-series data. 6. **Variable Workloads**: Applications with unpredictable or highly variable traffic patterns. **Real-World Examples**: - [Lyft's ride tracking system](https://aws.amazon.com/solutions/case-studies/lyft/) - [Snapchat's messaging infrastructure](https://aws.amazon.com/solutions/case-studies/snap-dynamodb/) ### When to Choose MongoDB MongoDB is particularly well-suited for: 1. **Complex Data Structures**: Applications with complex, hierarchical data models. 2. **Evolving Schemas**: Applications where data structure changes frequently. 3. **Complex Queries**: Applications requiring rich query capabilities and aggregations. 4. **Content Management**: Systems managing varied content types with different attributes. 5. **Multi-Cloud or Hybrid Deployments**: Applications requiring database portability. 6. **Prototyping and Rapid Development**: Projects needing quick iteration and flexibility. **Real-World Examples**: - [Forbes' content management system](https://www.mongodb.com/blog/post/forbes-cloud-migration-helps-worlds-biggest-media-brand-continue-standard-digital-innovation) - [SEGA's game analytics platform](https://www.mongodb.com/solutions/customer-case-studies/sega) ## Conclusion DynamoDB excels in AWS environments requiring predictable performance with minimal management overhead and automatic scaling, making it ideal for serverless architectures and applications with straightforward access patterns. MongoDB offers greater flexibility with its rich query capabilities, deployment options, and document model, better serving complex data relationships and queries while avoiding vendor lock-in.